A statistical program is recommended.

Spring is a peak time for selling houses. Suppose the data below contains the selling price, number of bathrooms, square footage, and number of bedrooms of 26 homes sold in Ft. Thomas, Kentucky, in spring 2018.

Selling PriceBathsSq FtBeds160,0001.51,7863170,00021,7683178,00011,2193182,50011,5782195,1001.51,1254212,50021,1962245,90022,1283250,00031,2803255,00021,5963258,0003.52,3744267,0002.52,4393268,00021,4704275,00021,6684Selling PriceBathsSq FtBeds295,0002.51,8603325,00032,0564325,0003.52,7764328,40021,4084331,0001.51,9723344,5002.51,7363365,0002.51,9904385,0002.53,6404395,0002.51,9084399,00022,1083430,00022,4624430,00022,6154454,0003.53,7004

Consider the estimated regression equation we developed that can be used to predict the selling price given the number of bathrooms, square footage, and number of bedrooms in the house.(x₁

denotes number of bathrooms,x₂

denotes square footage,x₃

denotes number of bedrooms, andydenotes the selling price.)

=6078.76+3656.82x₁+65.99x₂+44173.81x₃

(a)

Does the estimated regression equation provide a good fit to the data? Explain. (Round your answer to two decimal places.)

Since the adjustedR²

=, the estimated regression equation

---Select---

provides

does not provide

a good fit.

(b)

Consider the estimated regression equation that was developed which predicts selling price given the square footage and number of bedrooms.(x₂

denotes square footage,x₃

denotes number of bedrooms, andydenotes the selling price.)

=7392.39+67.63x₂+45217.77x₃

Compare the fit for this simpler model to that of the model that also includes number of bathrooms as an independent variable. (Round your answer to two decimal places.)

The adjustedR²

for the simpler model is, which is

---Select---

smaller

larger

than the adjustedR²

in part (a). The model from part

?

a

b

is preferred.